{"id":13223,"date":"2026-03-30T00:16:27","date_gmt":"2026-03-30T00:16:27","guid":{"rendered":"https:\/\/techtrendfeed.com\/?p=13223"},"modified":"2026-03-30T00:16:27","modified_gmt":"2026-03-30T00:16:27","slug":"much-less-gaussians-texture-extra-4k-feed-ahead-textured-splatting","status":"publish","type":"post","link":"https:\/\/techtrendfeed.com\/?p=13223","title":{"rendered":"Much less Gaussians, Texture Extra: 4K Feed-Ahead Textured Splatting"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<p>Current feed-forward 3D Gaussian Splatting strategies predict pixel-aligned primitives, resulting in a quadratic progress in primitive rely as decision will increase. This basically limits their scalability, making high-resolution synthesis corresponding to 4K intractable. We introduce LGTM (Much less Gaussians, Texture Extra), a feed-forward framework that overcomes this decision scaling barrier. By predicting compact Gaussian primitives coupled with per-primitive textures, LGTM decouples geometric complexity from rendering decision. This method allows high-fidelity 4K novel view synthesis with out per-scene optimization, a functionality beforehand out of attain for feed-forward strategies, all whereas utilizing considerably fewer Gaussian primitives.<\/p>\n<ul class=\"links-stacked\">\n<li>\u2020 The College of Hong Kong<\/li>\n<li>** Work achieved whereas at Apple<\/li>\n<\/ul>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Current feed-forward 3D Gaussian Splatting strategies predict pixel-aligned primitives, resulting in a quadratic progress in primitive rely as decision will increase. This basically limits their scalability, making high-resolution synthesis corresponding to 4K intractable. We introduce LGTM (Much less Gaussians, Texture Extra), a feed-forward framework that overcomes this decision scaling barrier. By predicting compact Gaussian primitives [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":13225,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[55],"tags":[8439,8437,8441,8438,8440],"class_list":["post-13223","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-feedforward","tag-gaussians","tag-splatting","tag-texture","tag-textured"],"_links":{"self":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/13223","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=13223"}],"version-history":[{"count":1,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/13223\/revisions"}],"predecessor-version":[{"id":13224,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/13223\/revisions\/13224"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/media\/13225"}],"wp:attachment":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13223"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13223"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13223"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<!-- This website is optimized by Airlift. Learn more: https://airlift.net. Template:. Learn more: https://airlift.net. Template: 69d9690a190636c2e0989534. Config Timestamp: 2026-04-10 21:18:02 UTC, Cached Timestamp: 2026-05-27 11:03:20 UTC -->